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中华肝脏外科手术学电子杂志 ›› 2023, Vol. 12 ›› Issue (01): 61 -67. doi: 10.3877/cma.j.issn.2095-3232.2023.01.012

临床研究

基于SEER数据库构建早期肝内胆管细胞癌预后Nomogram模型
王建磊1, 马德林1, 靳斌1,()   
  1. 1. 250000 济南,山东大学齐鲁医院器官移植科
  • 收稿日期:2022-10-19 出版日期:2023-02-10
  • 通信作者: 靳斌
  • 基金资助:
    国家卫生健康委课题合作项目(GWJJ2021100302)

Establishment of Nomogram prognostic model for early intrahepatic cholangiocarcinoma based on SEER database

Jianlei Wang1, Delin Ma1, Bin Jin1,()   

  1. 1. Department of Organ Transplantation, Qilu Hospital of Shandong University, Jinan 250000, China
  • Received:2022-10-19 Published:2023-02-10
  • Corresponding author: Bin Jin
目的

建立早期肝内胆管细胞癌(ICC)患者癌症特异性生存(CSS)的Nomogram预后预测模型,并验证其预测效能。

方法

本研究从美国国立癌症研究所监测、流行病学及结局项目数据库(SEER)中筛选出943例早期ICC患者资料。按照7∶3比例随机分为训练组(663例)和验证组(280例)。训练组采用单因素及多因素Cox分析筛选独立危险因素,并建立基于独立危险因素的预后Nomogram模型。通过一致性指数(C-index)、ROC曲线、校准曲线验证Nomogram模型的准确性。采用决策分析曲线(DCA)来评价Nomogram模型的临床实用性,并与美国癌症联合委员会(AJCC)分期进行比较。根据Nomogram模型计算的总分对患者进行风险分层,Kaplan-Meier生存曲线分析模型的分层效果。

结果

Cox回归分析显示,年龄、性别、婚姻状态、肿瘤直径、组织学分级、手术及放疗是早期ICC患者CSS的独立影响因素(HR=1.364,1.237,0.555,1.269,1.350,0.244,0.587;P<0.05)。基于危险因素构建患者CSS的Nomogram预测模型,训练组C-index为0.724,验证组为0.676;训练组及验证组1、3、5年ROC曲线的曲线下面积(AUC)均大于0.7。校准曲线分析显示,Nomogram预测结果具有良好的一致性。DCA分析显示,Nomogram模型具有良好的临床应用前景。与AJCC分期相比,该模型准确度和临床应用价值较高。建立风险分层系统,患者分为高、中、低危险组。Kaplan-Meier生存曲线分析显示,患者低、中、高危组的1年CSS率分别为88.4%、65.5%和35.5%,3年CSS率分别为63.4%、32.0%和7.6%,5年CSS率分别为48.2%、20.4%和4.5%,3组间CSS率差异有统计学意义(χ2=332.27,P<0.05)。

结论

基于SEER数据库成功构建早期ICC预后Nomogram模型,该模型较传统AJCC分期有更好的预测效能,且可对患者生存风险进行分层分析。

Objective

To establish a Nomogram prognostic model for the cancer-specific survival (CSS) of patients with early intrahepatic cholangiocarcinoma (ICC) and to validate the predictive efficacy.

Methods

Clinical data of 943 patients with early ICC were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database of American National Cancer Institute (NCI). According to the ratio of 7∶3, they were randomly divided into the training (n=663) and validation groups (n=280). In the training group, the independent risk factors were screened by univariate and multivariate Cox's regression analyses, anda Nomogram prognostic model was established based on these independent risk factors. The accuracy of this model was validated by C-index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility of the Nomogram model was assessed by decision curve analysis (DCA) and compared with the American Joint Committee on Cancer (AJCC) staging. According to the total score calculated by Nomogram model, all patients were subjected to the risk stratification. The effect of risk stratification was evaluated by Kaplan-Meier survival curve.

Results

Cox's regression analysis showed that age, sex, marital status, tumor diameter, histological grading, surgery and radiotherapy were the independent influencing factors for the CSS of patients with early ICC (HR=1.364, 1.237, 0.555, 1.269, 1.350, 0.244, 0.587; P<0.05). Based on these independent risk factors, the Nomogram prediction model of CSS patients was established. In the training group, the C-index was 0.724, and 0.676 in the validation group. The area under the ROC curve (AUC) at 1, 3 and 5 years exceeded 0.7 in two groups. The calibration curve analysis demonstrated that the predictive results of Nomogram model were in good agreement. DCA analysis revealed that Nomogram model had high clinical utility. Compared with the AJCC staging, this model yielded higher accuracy and clinical application value. A risk stratified system was established, and all patients were divided into the high, middle and low risk groups. Kaplan-Meier survival curve showed that the 1-year CSS in the low, middle and high risk groups were 88.4%, 65.5% and 35.5%, 63.4%, 32.0% and 7.6% for the 3-year CSS, and 48.2%, 20.4% and 4.5% for the 5-year CSS, respectively. Significant differences were observed in the CSS among three groups (χ2=332.27, P<0.05).

Conclusions

Based on the SEER database, the Nomogram prognostic model for early ICC has been successfully established. Compared with conventional AJCC staging, this model yields higher predictive efficiency, which can also deliver stratified analysis of survival risk.

表1 训练组早期ICC患者CSS的单因素及多因素Cox回归分析
图1 早期ICC患者CSS预后Nomogram列线图 注:ICC为肝内胆管细胞癌,CSS为癌症特异性生存
图2 早期ICC患者CSS的Nomogram模型ROC曲线 注:ICC为肝内胆管细胞癌,CSS为癌症特异性生存,AUC为曲线下面积
图3 早期ICC患者CSS的Nomogram模型校准曲线 注:ICC为肝内胆管细胞癌,CSS为癌症特异性生存
图4 早期ICC患者CSS的Nomogram模型DCA 注:ICC为肝内胆管细胞癌,CSS为癌症特异性生存,DCA为决策分析曲线,AJCC为美国癌症联合委员会
图5 早期ICC患者CSS风险分级Kaplan-Meier曲线 注:a、b、c分别为总体、训练组、验证组CSS风险分级Kaplan-Meier曲线;ICC为肝内胆管细胞癌,CSS为癌症特异性生存
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